Market Structure and the Environmental
Implications of Trade Liberalization:
Russia’s Accession to the World Trade Organization
Running title: “Environmental Implications of Russia’s WTO Accession”
Christoph Böhringer∗
Professor of Economics, University of Oldenburg
Thomas F. Rutherford†
Professor of Agricultural and Applied Economics, University of Wisconsin-Madison
David G. Tarr
Former Lead Economist, The World Bank
Natalia Turdyeva
Researcher, Center for Economics and Financial Research, Moscow
August 10, 2015
Abstract
We investigate the environmental impacts of Russia’s WTO accession with a computable general
equilibrium model incorporating imperfectly competitive firms, foreign direct investment, and endogenous productivity. WTO accession increases CO2 emissions through technique (−), composition
(+) and scale (+) effects. We consider three complementary policies to limit CO2 emissions: cap and
trade; emission intensity standards; and energy efficiency standards. With imperfectly competitive
fimrs, gains from WTO accession result with any of these policies. If we assume perfectly competitive
market structures, the negative environmental impacts of WTO accession are smaller, and yet no net
gains arise when environmental regulation involves energy intensity or efficiency standards.
Key Words: trade and the environment; environmental regulation; imperfect competition; increasing returns to scale.
JEL classification: F18; F13; F12; Q52; Q58; C68.
1
Introduction
General equilibrium considerations can have first order importance in the evaluation of trade or environmental policies. Thus, computable general equilibrium (CGE) models are widely used to quantify the
∗
The authors would like to thank Craig Meisner, Adriana Damianova and Kulsum Ahmed for their insights and guidance.
The authors gratefully acknowledge the financial support for this project (P133836) provided through a Research Budget Grant
from the World Bank’s Development Research Administration (DECRA). The views expressed are those of the authors and do
not necessarily reflect those of the World Bank or its Executive Directors.
†
Corresponding author. Address: 330 Taylor Hall, 427 Lorch Street, Madison, WI 53706-1503; Phone +1-608-616-2344; Fax
+1-608-262-4376; Email: [email protected]
1
prospective economic and environmental impacts of policy regulations such as trade reforms or emission
control policies. The bulk of CGE assessments, however, is based on models that employ constant-returnsto-scale (CRTS) and perfectly competitive markets.1 Of the fifty-four CGE assessments of climate change
policy surveyed by Carbone and Rivers [2014], only Babiker [2005] and Böhringer, Loschel, and Welch
[2008] incorporated imperfect competition.2 As a result, nearly all assessments omit potentially important impacts of increasing returns to scale (IRTS), where endogenous productivity effects can significantly
alter the results.
The integrated trade and environment model applied here contains the features of Krugman [1980]
and Ethier [1982] by incorporating increasing returns to scale, monopolistic competition, and endogenous productivity effects from additional varieties of goods in imperfectly competitive sectors. Crucially,
however, our model also incorporates foreign direct investment (FDI) in business services with endogenous productivity effects from additional varieties of business services. We thereby reflect the theoretical
analysis of Markusen [1989], Francois [1990], and Markusen, Rutherford, and Tarr [2005] in which the
greater availability of business services results in total factor productivity gains to the manufacturing sector.
Remarkably, the theoretical literature is supported by a growing body of empirical literature, including
several studies using firm-level data, which has found a significant positive impact on total factor productivity from additional FDI in business services.3
To our best knowledge, our model is the first CGE model designed for trade and environmental policy
analysis to incorporate these features. We use this model for the impact assessment of Russia’s accession
to the World Trade Organization (WTO). After eighteen years of negotiations, Russia joined the World
Trade Organization in 2012. Russia’s accession agreement calls for wide-ranging reforms that will be implemented in phases through 2020.4 Given that the Government of the Russian Federation’s Program
on Environmental Protection 2012-2020 emphasizes the need for environmental policies to accompany
economic growth to achieve sustainable development,5 policy makers in Russia are concerned about the
environmental effects of WTO accession, and may wish to implement regulation in ordert to mitigate
potentially negative implications for the environment.
In their impact analysis of NAFTA for Mexico, Grossman and Krueger [1993] provide evidence on the
endogeneity of trade liberalization and environmental policy in that trade liberalization induced greater
environmental regulation. Their observation is given substantial support by the econometric analysis
of Antweiler, Copeland, and Taylor [2001] who examine sulphur dioxide emissions in more than forty
countries following trade liberalization. They find that there is a decrease in emissions following trade
liberalization, largely because of stricter environmental regulation.
Acknowledging the endogeneity of environmental policy with trade liberalization, and the stated objective of the Russian government to reduce CO2 emissions (see section 2), our analysis goes beyond an
isolated impact assessment of Russia’s WTO accession. More specifically, we combine the policy changes of
WTO accession with three alternate environmental regulations designed to reduce CO2 emissions: marketbased cap and trade; emission intensity standards; and energy efficiency standards. We also separately
estimate the impacts of these three emission abatement policies independent of WTO accession. Our sim1
For surveys, see Hertel [1997], Conrad [2001], Dean [2002], Bovenberg and Goulder [2002], Bergman [2005], Frankel and
Rose [2005], and Carbone and Rivers [2014].
2
Babiker employs a model of Cournot oligopoly to assess the economic impacts of the Kyoto Protocol. His model allows
rationalization gains or losses, which magnifies the losses of countries limiting their emissions compared with perfect competition. Böhringer et al. also employ a model of Cournot oligopoly and similarly find that the losses from emission regulation are
magnified with imperfect competition.
3
See Francois and Hoekman [2010] for a survey of empirical studies that support the finding of productivity effects from
additional business services. Studies that use firm-level data include Arnold, Javorcik, and Mattoo [2011] for the Czech Republic,
Fernandes and Paunov [2012] for Chile, Shepotylo and Vakhitov [forthcoming] for Ukraine and Arnold, Javorcik, Lipscomb,
and Mattoo [forthcoming] for India.
4
See Tarr and Volchkova [2013].
5
See of Natural Resources and of the Russian Federation [2014]
2
ulations should help authorities to design policies that minimize the economy-wide costs of achieving a
given level of emission reduction, and thereby address political resistance to environmental regulation.
One of our key results is that, in the simulations combining WTO accession with the two less efficient
emission abatement policies of setting standards (rather than adopting cap and trade), the sign of the impact
on the net welfare effects depends on the underlying market structure. Contrary to the results in an IRTS
setting with imperfect competition, WTO accession does not generate sufficient gains in the CRTS setting
with perfect competition to provide net benefits when the costs of the less efficient emission abatement
policies are incorporated. While our results are consistent with earlier studies that suggest increased pollution with IRTS models (absent offsetting environmental regulations), our analysis indicates that there
are greater gains available when taking IRTS into account, which allow for net gains after regulation to
produce a net cleaner environment.
The remainder of the paper is organized as follows. In section 2 we summarize the environmental
policy debate in Russia which explains the interest of Russian policy makers in potential trade-offs between trade liberalization and environmental quality. In section 3 we present the CGE model and the data
sources underlying our impact assessment of Russia’s WTO accession. In section 4 we motivate the policy
scenarios to be investigated and discuss simulation results from the IRTS model with imperfect competition. In section 5 we provide a comparison with the resuts obtained from the CRTS model with perfect
competition. In section 6 we assess the sensitivity of our results to key parameter assumptions. In section
7 we conclude.
2
Environmental Policy Debate in Russia
Russia is the largest country in the world spanning nine time zones with a population of more than 140
million people. It is also one of the richest countries in the world for oil, gas, and a wide range of minerals.
Russia’s natural endowment has been a key driver of economic growth in the current millennium, based on
production and export of raw materials. As a legacy of the Soviet-period, many industries are characterized
by high resource use, very high energy intensities, and obsolete production processes. The quality of
environmental conditions in Russia is inadequate on about 15% of the country’s territory which is home
to 57% of the population.6 Due to high levels of air pollution, the estimated average life expectancy of
the population is reduced by about one year, while in more polluted cities by about four years. It is
estimated that this factor is the reason for about 8% of deaths annually. The main sources of air pollution
are particulate matter, sulphur dioxide, and nitrogen oxide linked primarily to fossil fuel combustion (IFC
[2008]).
Russia is currently the world’s fourth-largest emitter of greenhouse gas emissions. While Russia ratified the Kyoto Protocol in 2004,7 it refused so far to commit to internationally binding Post-Kyoto commitments. Nonetheless, Russia is still pursuing a national greenhouse gas emission reduction program
of between 15% and 25% from 1990 emission levels by 2020 as well as a broader set of policies for sustainable development.8 Along with emission reduction targets, the Russian government emphasizes the
need for increased investments in energy and resource efficiency,9 cleaner technologies, recycling and reuse
of wastes. Against this background, Russia’s WTO accession has engendered concerns about potentially
negative environmental impacts.
According to Rosgidromet [2013]: “In 138 cities in Russia (57% of the urban population), the level of air pollution is characterized as high or very high.”
7
On November 5, 2004, Russia ratified the Kyoto Protocol, which facilitated the Protocol entering into force in February
2005. Under the Kyoto Protocol, Russia committed to limit its annual emissions of greenhouse gases at 1990 emission level –
3,048 million tons of CO2 equivalent (MtCO2e) – over the period 2008-2012.
8
See of Natural Resources and of the Russian Federation [2014].
9
See of Energy of the Russian Federation [2010].
6
3
3
CGE Model for Russia: Structure and Data
For the economic and environmental impact assessment of Russia’s WTO accession we extend a 10-region,
30-sector computable general equilibrium model of the Russian economy (Rutherford and Tarr [2010])
by incorporating emissions data and by allowing inter-fuel substitution in production and consumption
activities. Below, we provide a non-technical model summary10 and describe the data sources for model
parameterization.
3.1
3.1.1
Model Structure
Sectors, Regions, and Primary Factors of Production
There are 30 sectors in the model that are listed in Table 1. We group several contiguous regions of Russia
into ten “Regional Markets” listed in Table 3.11 There are three types of sectors: perfectly competitive
goods and services, imperfectly competitive goods, and imperfectly competitive business services (with
FDI). We assume that firms operate at the Regional Market level.
Primary factors include skilled and unskilled labor and three types of capital: mobile capital (within
regions); sector-specific capital in the energy sectors reflecting the exhaustible resource; and sector-specific
capital in imperfectly competitive sectors. We also have primary inputs imported by multinational service
providers, reflecting specialized management expertise or technology of the firm. The existence of sectorspecific capital in several sectors implies that there are decreasing returns to scale in the use of the mobile
factors and supply curves in these sectors slope up. Labor is assumed immobile across regions.
As to factor earnings, the representative agent in the region obtains the returns from skilled and unskilled labor employed in the respective region. We assume that half of all the capital in any region is held
by the representative agent in the region and the other half by a national mutual fund. The national mutual fund invests in all regions and obtains an overall return. The representative agent in the region also
holds shares in the national mutual fund.12
For each region we report returns to capital as returns to the three types of regional capital held by the
region’s representative agent. In addition, the region’s representative agent obtains a share of the returns
from the national mutual fund. The region’s return from national capital is the region’s share of the return
of the national mutual fund reported as a percentage of initial consumption of the region.
3.1.2
Non-Fossil Fuel Sectors
In Figure 1, we depict the structure of production for non-fossil fuel sectors where substitution possibilities
on the input side are captured by constant elasticities of substitution (CES) and transformation possibilities
on the ouput side are prescribed by constant elasticities of transformation (CET).13 Regional firms use
intermediate inputs (which can be foreign inputs, inputs from other regions of Russia or from their own
region) and primary factors of production to produce output. We emphasize that business services are not
part of the “composite of other goods and services”; rather business services substitute for primary factors
of production.14 This structure allows the model to capture the econometric evidence, cited above, that
A detailed algebraic description of the core model is available as an appendix to Jensen, Rutherford, and Tarr [2004].
For details of the mappings and data, see Tables 2-11 in Böhringer, Thomas F. Rutherford, and Turdyeva [2014].
12
See Rutherford and Tarr [2010] for an explanation of the representative agent’s share of the income of the national mutual
fund.
13
For fossil fuel production, we assume decreasing returns to scale: Value-added and all goods and services form a Leontief
composite input that substitutes in a CES nest with a sector-specific resource. The elasticities of substitution in fossil-fuel sectors
are calibrated to match exogenous estimates of fossil-fuel supply elasticities.
14
For example, firms can employ an accountant or a lawyer, or contract for accounting or legal services. They can employ
a driver and buy a truck, or contract for delivery services. These examples make it evident that it is more appropriate to allow
substitution between business services and primary factors of production than to assume a Leontief structure.
10
11
4
greater access to business services increases productivity in the firms that use business services. We show
in our sensitivity analysis that the elasticity of substitution between business services and primary factors
of production significantly impacts the results. We generalize Rutherford and Tarr [2010] by allowing
for inter-fuel substitution which is central to the analysis of greenhouse gas emissions, in partiucalr CO2
emissions from the combustion of fossil fuels.
3.1.3
Perfectly Competitive Goods and Services
Firms in each Regional Market have three choices for sales: sell in their own Regional Market; sell to other
parts of Russia; or export to the rest of the world. Firms maximize revenue for any given output level
based on their transformation possibilities between goods for the three markets. For all firms within the
same Regional Market, the product they export to all other Regional Markets of Russia is homogeneous.
It follows that for each competitive good, there will be only three prices: the price of good g in Regional
Market r ; the price of good g from Regional Market r in all other parts of Russia; and the price of good
g from Regional Market r in the rest of the world.
Economic agents in a representative Regional Market r employ multi-stage budgeting based on nested
CES expenditure functions. They optimize expenditures on foreign goods versus goods from Russia, then
between goods from other Russian Regional Markets and their own Regional Market, and also between
goods from the other Russian Regional Markets. This structure assumes that consumers differentiate the
products of producers from different Regional Markets; but, they regard as homogeneous the products of
different producers from the same Regional Market.
3.1.4
Imperfectly Competitive Goods
Economic agents in each Regional Market optimally allocate expenditures among the goods available from
the different Regional Markets of Russia and the rest of the world producers. Since we assume identical
elasticity of substitution at all levels, this is equivalent to firm-level product differentiation of demand.
That is, the structure is equivalent to a single stage in which agents decide how much to spend on the
output of each firm.
We assume that imperfectly competitive manufactured goods may be produced in each region or imported. Both Russian and foreign firms in these imperfectly competitive industries set prices such that
marginal cost (which is constant with respect to output) equals marginal revenue in each Regional Market.
There is a fixed cost of operating in each region and there is free entry, which drives profits to zero for each
firm on its sales in each Regional Market in which it sells: Quasi-rents just cover fixed costs in each region
in the zero-profit equilibrium. We assume that all firms that produce from the same Regional Market have
the same cost structure, but costs differs across regions.
Foreigners produce the goods abroad at constant marginal cost but incur a fixed cost of operating in
each Regional Market in Russia. Becuase of free entry, in equilibrium the import price (less tariffs) must
cover fixed and marginal costs that foreign firms incur in each Regional Market.
Similar to foreign firms, Russian firms also produce their goods in their home regions; they incur a
fixed cost of operation in each Regional Market in which they operate. Due to free entry there are zero
pure profits, and the product price must just cover both fixed and marginal costs of operation in that
Regional Market.
We assume that Russian firms do not have any market power on world markets and thus act as price
takers on their exports to world markets. On exports to the rest of the world, price thus equals marginal
costs. On sales to Russia, firms must use a specific factor in addition to the other factors of production.
The existence of the specific factor implies that additional output of firms can only come at increasing
marginal costs. Imperfectly competitive Russian goods producers sell in all of Russia; but services firms
do not sell in other Russian Regional Markets.
5
We employ the standard Chamberlinian large-group monopolistic competition assumption within a
Dixit-Stiglitz framework, which results in constant markups over marginal cost. For simplicity we assume
that the ratio of fixed to marginal cost is constant with respect to all factors of production for all firms
producing under increasing returns to scale (in both goods and services). This assumption in a DixitStiglitz based Chamberlinian large-group monopolistic competition model assures that output per firm
for all firm types remains constant, i.e., the model does not produce rationalization gains or losses. Similar
to the Krugman (1980) model, our results will however differ from CRTS results to the extent that there
are endogenous technology spillovers from the Dixit-Stiglitz variety effect.
An increase in the number of varieties increases the productivity of the use of imperfectly competitive
goods based on the standard Dixit-Stiglitz formulation. The cost function for users of goods produced
subject to increasing returns to scale declines in the total number of firms in the industry. The lower the
elasticity of substitution, the more valuable is an additional variety.
We assume that imperfectly competitive firms within a Regional Market have symmetric cost structures and face symmetric demand for their outputs. It follows that all imperfectly competitive firms from
a Regional Market will obtain the same price in any Regional Market of Russia in which they operate,
although the price will differ across Regional Markets since the fixed costs associated with entering any
Regional Market varies across the Regional Markets.
3.1.5
Imperfectly Competitive Business Services with FDI
Imperfectly competitive business services are supplied by foreign service providers on a cross-border basis,
but a large share of business services are supplied by service providers with a domestic presence, both
multinational and Russian.15 Our model allows for both types of foreign service provision in these sectors.
Cross-border services from the rest of the world, however, are not good substitutes for service providers
who have a presence within the Regional Market of Russia where consumers of these services reside.
Russian firms providing imperfectly competitive business services operate at the regional level and organize production in a manner fully analogous to imperfectly competitive Russian firms producing goods.
Thus, Figure 1 applies to both Russian imperfectly competitive goods and services firms. Other assumptions we made for imperfectly competitive goods producers, such as entry conditions, pricing and symmetry also apply to imperfectly competitive services providers. The only difference is that we assume that
regional services providers sell only in their own Regional Market or export.
Multinational service firm providers that choose to establish a presence in a Regional Market of Russia
incur a fixed cost of operating in a Regional Market. As with imperfectly competitive goods producers,
quasi-rents must cover the fixed plus marginal costs of producing in a Regional Market and due to free
entry we have a zero-profit equilibrium.
For multinational firms, the barriers to foreign direct investment affect their profitability and entry.
Reduction in the constraints on foreign direct investment in a region will induce foreign entry that will
typically lead to productivity gains because when more varieties of service providers are available, buyers
can obtain varieties that more closely fit their demands and needs (the Dixit-Stiglitz variety effect).
3.1.6
Trade
Trade flows for perfectly competitive goods between regions and the rest of the world are specified following the Armington [1969] approach, which distinguishes goods by origin. Firm-level product differentiation characterizes trade for imperfectly competitive sectors. Regions are assumed to be price takers
in the world market – i.e., the representation of the rest of the world is reduced to perfectly elastic import
Drusilla K. Brown [2001] estimate the world-wide cross-border share of trade in services at 41% and the share of trade in
services provided by multinational affiliates at 38%. Travel expenditures 20% and compensation to employees working abroad
1% make up the difference.
15
6
demand and export supply functions (export and import prices from the rest of the world are exogenous).
Each region has a balance of trade constraint so that any change in the value of imports (either from the rest
of the world or another Regional Market within Russia) is matched by an increase in the value of exports.
Russia as a whole, represented as an aggregate of the Regional Markets, must satisfy an economy-wide balance of the trade constraint. The real exchange rate adjusts to assure that any change in the aggregate value
of regional imports from the rest of the world, is matched by an equal change in the value of aggregate
exports to the rest of the world.
3.1.7
Government
Government demand across all regions is fixed at exogenous real levels. The government receives taxes to
finance public expenditures. Public surpluses or deficits are balanced through lump-sum transfers with the
representative households in each region.
3.1.8
Emissions
The model tracks CO2 emissions as well as six non-CO2 pollutants: sulphur dioxide, nitrogen oxide,
hydrocarbons, particulate matter, volatile organic components, and carbon monoxide. CO2 emissions,
which are by far the most important greenhouse gas emissions for Russia, are linked in fixed proportions
to the use of coal, natural gas and refined oil products, with CO2 coefficients differentiated by the specific
carbon content of the fuels. CO2 abatement can take place by fuel switching (inter-fuel substitution) or
energy savings (either by fuel-non-fuel substitution or by a scale reduction of production and final demand
activities or by more efficient production technologies that use less fuel per unit of output). All non-CO2
emissions are linked in fixed proportions to output.
3.2
Data
Base-year input–output data determines the free parameters of the cost and expenditure functions such that
the economic flows represented in the data are consistent with the optimizing behavior of the economic
agents. In the following, we lay out our central data sources.
3.2.1
National Input-Output Table
The core input-output data are from the national 2001 input-output table produced by Russian statistical
office Rosstat containing only 22 sectors and little service sector disaggregation. In order to disaggregate
the input-output table, we draw on cost shares and use shares from an expanded 35 sector Russian inputoutput table for 1995 (Rutherford and Tarr [2008]). To break up a composite sector such as oil and gas
into specific subsectors oil, gas, and oil processing, we assume that the cost shares and use shares of the
sector are the same in 2001 as they are in the 1995 table.
3.2.2
Regional Input-Output Tables and Trade Flows of the Regions
We obtain data on 88 regions of Russia from Regions of Russia published by Rosstat [2003]. We then
construct input-output tables of the 88 regions that are based on data from the regions and the national
input-output table. The input-output tables for our ten Regional Markets are aggregates of the inputoutput tables of the regions in their respective Regional Markets.
We assume that the technology of production is common across regions, so that the input-output
coefficients from the national input-output table apply across all regions. As a first step, for each industrial
sector, we take the national output from the national input-output table for 2001, and we use the data in
Regions of Russia to allocate the shares of that output across the 88 regions. That is, from the Rosstat [2001],
we have, by region, the value of total industrial output and industry shares of regional industrial output
7
for the year 2000 (see Table 13.3. in Rosstat [2001])16 This allows us to calculate the value of industry
output by sector and region. For each industrial sector, we then proportionally scale the value of regional
output so that the sum of industrial output across all regions is equal to the value of national output of the
sector from the national input-output table.
We infer regional demand (and supply) of services, assuming that intermediate and final demand for
services share a common intensity of demand in all regions as in the national model. For example, if
telecommunications costs are x percent of the costs of nonferrous metals production in the national model,
we assume that telecommunications costs are x percent of nonferrous metals costs in each of the regions.
Demand for telecommunications from nonferrous metals will differ across regions, however, since the
share of total output attributable to nonferrous metals differs across regions.
We have total external exports and imports by region, as well as the commodity structure of external
exports and imports by region for the year 2001 (Tables 23.1 and 23.2 in Rosstat [2001]). We also use
Rosstat data on inter-regional exports and imports by sector. That is, for each of over 250 key commodities,
we have an 88 by 88 matrix of bilateral trade flows among the regions.17
Supply and demand balance by region and by commodity requires adjustment of regional import and
export trade intensities. These adjustments assure that region exports and imports in aggregate are consistent with national import and export values. We do this using a methodology that minimizes the sum of
squares of the difference between the original data on exports and imports and the adjusted exports and
imports data, subject to the constraints of supply-demand balance and consistency with the national model
data. Since we have greater confidence in the validity of the region output data than the inter-region trade
flow matrix, in this optimization process, we fix the output levels of the regions at the levels we calculated
before.
Since in every step of the process we calculate region shares of the national input-output table, we
may aggregate the 88 regions into a set of non-overlapping subsets and any such aggregation will yield a
set of input-output tables that is fully consistent with the national input-output table. In particular, our
10-region, 30-sector model is consistent with the national input-output table.18
3.2.3
Emissions Data
With respect to environmental impacts of trade liberalization and the economic implications of emission
control policies, emission intensities play a key role. The more emission intensive a sector is, the more
adversely its production should be affected by pricing of emission inputs.Another key determinant of
adjustment cost is the ease of substituting away from emission inputs as described through the nesting
structure and the elasticities of substitution between inputs. In Table 1, we show emission intensities across
Russian industries (the national average).19 CO2 emission intensities are composed of direct emissions
from fossil fuel inputs, as well as indirect emission embodied in electricity inputs. Emission intensities in
coal and gas production, electricity generation and pipeline transportation (including gas leakage) are the
highest. Indirect emissions embodied in electricity play a secondary role for most sectors. Table 1 also
shows emission intensities for non-CO2 pollutants across industries.20 Electricity generation, nonferrous
and ferrous industries rank highest for sulphur dioxide emissions. These sectors also show substantial
emission intensities for carbon monoxide, where crude oil production has by far the highest intensity. The
16
The same publication provides information for the year 2000 of oil recovery and mined coal in thousands of tons and on
extraction of natural gas (in millions of cubic meters).
17
For the data, see http://sophist.hse.ru/rstat_data/archbase/natur01/1viv1-n/BBF1_10.htm
18
For multinational shares of services sectors in the ten regions of Russia of our model, we obtain the shares of workers working
in multinationals service sectors in each sector from the Russian government’s survey known as the NOBUS survey and combine
this with the estimates of the Russian service sector institutes mentioned above. See Rutherford and Tarr [2010] for details.
19
See Böhringer, Thomas F. Rutherford, and Turdyeva [2014] for a detailed explanation of the data sources and calculation of
emissions at the sector and regional level.
20
Non-CO2 emissions that are not listed at the sector level are assumed to be negligibly small.
8
release of hydrocarbons is predominantly associated with coal production and transportation activities.
3.2.4
Ad Valorem Equivalence of Barriers to FDI in Services Sectors
The business services sectors have been the subject of some of the most intense negotiations associated
with Russian WTO accession. Russia has made numerous commitments in this area, including to foreign
firms that provide environmental services. In many cases, Russia has implemented changes in the services
sectors prior to accession to adapt to post-WTO requirements; in other cases, the commitments may be
implemented only several years after accession due to a negotiated adjustment period.21 The counterfactual
scenario that we implement in the business services sectors attempts to encapsulate all these cumulative
reforms.
Estimates of the ad valorem equivalents of these and other barriers to FDI in services are central to
the quantitative impact assessment of trade reforms. Building on a series of studies commissioned by the
Australian Productivity Commission (Findlay and Warren, 2000) we estimate the ad valorem equivalents
of barriers to foreign direct investment in several Russian sectors, namely in telecommunications; banking,
insurance and securities; and maritime and air transportation services.22 The results of the estimates and
the assumed reductions in the ad valorem equivalents of the barriers are listed in Table 2.
3.2.5
Tariff and Export Tax Data
Tariff rates by sector are based on the work of Shepotylo and Tarr [2013]. They estimate the applied most
favoured nation (MFN) tariff rates at the ten digit level in Russia for all years from 2001 to 2020. Based on
these estimates, they provide aggregated tariff rates for the sectors of our model. From 2001 to 2011 inclusive, the estimates are based on actual tariff rates and trade data in Russia. Russia will implement its tariff
commitments under its WTO accession agreement progressively from 2012 to 2020. For 2012 to 2020,
Shepotylo and Tarr estimate changes in the applied MFN tariff based on the progressive tariff reduction
commitments Russia has taken under its WTO accession agreement. Unlike in the case of services, there
is evidence that Russia’s tariff commitments did not impact its tariffs prior to 2012. Consequently, we take
tariffs in 2011 as the benchmark tariff rates. We take the tariff rates in 2020 (when all tariff commitments
are implemented) as the tariff rates in the counterfactual. These tariffs are reported in Table 2. Export tax
rates are calculated from the 2001 input-output table of Rosstat and are reported in Table 2.
4
Policy Scenarios and Simulation Results
4.1
Policy Scenarios
One of our principal interests is the quantification of economic and environmental impacts associated
with Russia’s WTO accession. In our WTO accession scenario (executed without emission abatement
policies) we assume that: (i) barriers to foreign direct investment are eliminated or reduced (depending on
the sector); (ii) applied tariffs will fall according to the commitments of the Russian Federation as part of
its WTO accession agreement; and (iii) for six Russian sectors that have been subjected to antidumping
actions in export markets, we postulate better access to export markets that will lead to higher export
prices ranging from 0.5 percent to 1.5 percent (see Table 2).
We then evaluate the cost-effectiveness of three alternate “green” policy initiatives in Russia that are
all designed to reduce CO2 emissions by twenty percent from base-year emission levels. These are: (i)
market-based cap and trade regulation; (ii) uniform emissions intensity standards; and (iii) uniform energy
efficiency standards. Cap and trade regulation imposes a twenty percent reduction of CO2 emissions
21
22
See Tarr and Volchkova [2013] for details.
For more details on the estimation, see Jensen, Rutherford, and Tarr [2007].
9
in Russia through a system of tradable emission rights (or equivalently a nation-wide CO2 tax).23 Since
marginal abatement costs of emissions are equilibrated across all users of fossil fuels under cap and trade,
it is the least costly environmental regulation possible to achieve the objective. In the case of uniform
emissions intensity standards, we require that all sectors and regions except for fossil fuels sectors (coal,
crude oil and gas,) uniformly reduce the intensity of their CO2 emissions per unit of the value of output
produced. We solve iteratively for the uniform reduction in the intensity of CO2 emissions such that a
twenty percent reduction in CO2 emissions is achieved. In the case of uniform energy efficiency standards,
we require that in all regions, all sectors except electricity and fossil fuel production (crude oil, coal and
natural gas) equi-proportionately reduce their use of gas, refined oil, and electricity per unit of output. As
with emissions intensity standards, we solve iteratively for uniform adjustments in energy intensity such
that a twenty percent reduction in CO2 emissions is achieved.24
As stated in the introduction, Antweiler, Copeland, and Taylor [2001] and Grossman and Krueger
[1993] provide evidence of endogeneity of trade and environment policies in that trade liberalization tends
to be accompanied by greater environmental regulation. In this vein, we also execute three additional
scenarios in which we assess the impacts of “overlapping” policy reforms. In these scenarios we simultaneously implement our WTO accession policy changes with each of our three CO2 emission reduction
policy initiatives. In particular, for the scenario that combines cap and trade with WTO accession policy
commitments, Table 3 presents the full economic and environmental impacts for all ten of our Regional
Markets for the scenario. Due to space constraints, we only present results for the other six policy scenarios for the Russian economy as a whole. These results are presented in Table 4. For the full Regional
Market results, see Böhringer, Thomas F. Rutherford, and Turdyeva [2014] where we show results for all
thirty sectors in all ten Regional Markets, including results for welfare, emissions of our seven pollutants,
changes in exports, imports, employment at the sector level, factor returns, and the real exchange rate.
4.2
Environmental and Economic Impacts of WTO Accession
4.2.1
Overall Economic Impacts
As shown in Table 3, if we combine the WTO policy changes with CO2 reduction by twenty percent
through cap and trade, the estimated overall welfare gains are 7.2 percent of consumption, with considerable differences across regions. In Table 4, we show the economy-wide estimated gains for all seven
scenarios. In particular, our cap and trade policy costs 1.4 percent of consumption since without additional environmental regulation, the estimated overall Russian economy average welfare gain from WTO
accession is 8.6 percent of consumption.25 Rutherford and Tarr [2008] reveal that the source of the largest
gains from Russia’s WTO accession is the reduction in barriers against foreign investors in services – accounting for about 70 percent of the total gains. Consistent with the econometric evidence cited above, in
our model new services providers increase the productivity of sectors that use services. Russian commitments to reduce barriers against multinational service providers will allow multinationals to obtain higher
after tax returns on their investments in Russia and thus initially create positive profits. This will induce
the multinationals to increase foreign direct investment to supply the Russian market until the additional
entry restores a zero profit equilibrium. Although we find that there is some decline in the number of
purely Russian owned businesses serving the services markets, on balance there will be additional service
providers. Russian users of business services will then have improved access to the providers of services in
23
A twenty percent CO2 emission reduction vis-à-vis base-year emission levels is roughly in line with the stringency of emission
reduction objectives communicated by the Russian government (of Natural Resources and of the Russian Federation [2014]).
24
We are precise with cap and trade, but allow for a small tolerance in the solution of up to one percentage point of CO2
emissions in either emissions intensity or energy intensity standards. That is, CO2 emissions are reduced by between 19 and 21
percent with the latter two regulatory policies.
25
Due to the possibility of inter-fuel substitution and optimization of export sales in the energy sectors that were not allowed
in Rutherford and Tarr [2010], the estimated welfare gains overall are 0.8 percent of consumption larger with the present model.
10
areas like telecommunication, banking, insurance, transportation and other business services.
4.2.2
Region-Specific Economic Impacts
The differences across Regional Markets in Table 3 are primarily explained by two considerations: (i) the
ability of the different Regional Markets to benefit from a reduction in barriers against foreign direct investment; and (ii) the differences in CO2 emissions intensities across regions. To diagnose the relative
importance, first consider WTO accession without environmental regulation. Then the Regional Markets
that gain the most from WTO accession as a percent of GDP are Northwest (5.6 percent), St. Petersburg
(5.2 percent) and Far East (4.8 percent). Our dataset shows that these three Regional Markets have the
largest shares of multinational investment (along with the North Regional Market, which also gains substantially).26 The Urals, which has relatively little FDI in the services sectors, has the lowest estimated
gains at 3.3% of GDP. On the other hand, under cap and trade without WTO accession policies, the welfare
losses (as a percent of GDP) are greatest for the Regional Markets of Saint Petersburg (-0.6%), Far East
(-0.8%) and Moscow (-1.0%). We find that the Northwest Regional Market is a net seller of emissions
permits, it does not lose welfare from cap and trade CO2 regulation. (It does lose welfare from the less
efficient emissions regulations.) Netting out the costs of CO2 regulation explains why the Northwest regions stands out as the principal gainer in terms of an increase in GDP from WTO accession combined
with cap and trade.
4.2.3
Decomposing the Impacts of WTO Accession and CO2 Emissions
Grossman and Krueger [1993] as well as Copeland and Taylor [1994] and Copeland and Taylor [1995]
decompose the overall environmental (emission) impact of trade liberalization into three components:
scale, composition, and technique. The scale effect refers to an increase in emissions associated with a
larger GDP, holding constant the relative mix of outputs and pollution intensities across sectors. The
composition effect refers to a change in the share of dirty goods in GDP. The technique effect refers to a
change in the amount of emissions per unit of output across sectors.
CRTS models are driven by the theory of comparative advantage and thereby focus primarily on composition effects.27 With increasing returns to scale and imperfect competition, however, both scale and
technique effects are more prominent. Productivity changes emerge through trade liberalization due to
rationalization gains or because the number of product varieties and technologies increase. With IRTS this
would likely lead to more pollution from the scale effect, but less resource use per unit of output and less
pollution from the technique effect.28 Further, if trade liberalization leads to an expansion of dirty industries subject to IRTS technology, pollution problems will be exacerbated due to the scale and composition
effects compared to a CRTS reference. On the other hand, IRTS will likely lead to greater income gains and
the capacity to pay for environmental regulation.
To gain insights into their direction and magnitude we quantify the scale, composition and technique
effects of WTO accession in Russia on CO2 emissions and compare the total and decomposed effects in
our central IRTS model with those generated in the more conventional CRTS model (see section 5). We decompose emissions into the scale, composition and technique effects based on the following methodology.
See Böhringer, Thomas F. Rutherford, and Turdyeva [2014] for the additional simulations and the multinational investment
shares.
27
Antweiler, Copeland, and Taylor [2001] find small composition effects from trade liberalization due to the fact that the
impacts from the Factor Endowment and Pollution Haven hypotheses tend to offset each other. See also the results of Frankel
and Rose [2005] and Dean [2002] for a survey of the trade and environment literature.
28
Martin [2012] econometrically assesses the implications of trade openness on greenhouse gas emissions by India’s manufacturing firms. She finds that the reduction in import tariffs led to improved fuel efficiency as fuel efficient manufacturing
firms gained market share whereas fuel-inefficient firms lost market share; within the manufacturing sector, improved capital
access tended to reduce fuel-intensity rather than increase it because of technological (energy-saving) progress embodied in new
vintages.
26
11
Let E = emissions, Y = the value of aggregate output; ai = emissions per unit of the value of output
of sector i; yi = the value of output of sector i; and θi = the share of sector i’s output in the economy.
With n sectors in the economy, aggregate emissions are:
E=
n
X
ai yi
(1)
i=1
since yi = θi Y , we have
E =Y
n
X
ai θi
(2)
i=1
It follows that, in the neighborhood of the equilibrium, the percentage change in emissions can be
decomposed into:
P
P
θ ∆a
∆E ∆Y
i ai ∆θi
=
+ P
+ Pi i i
(3)
E
Y
i ai θi
i ai θi
The first term on the right side of equation 3 is the scale effect; the second term is the composition
effect, and the third term is the technique effect. In all of our scenarios, we show the percentage change
in CO2 emissions in total and due to each of these three effects for each of the ten regions of our Russia
model and an overall average for the Russian economy.29
Tables 4 and 5 reveal that despite a positive impact on the environment from the technique effect, the
relative expansion of dirty industries in Russia and the scale effect dominate so that, in either our IRTS or
CRTS models, WTO accession alone will increase environmental pollution. Primarily due to larger output
expansion in our IRTS model, we estimate a larger increase in pollution from WTO accession in our IRTS
model.
We also show in section 4.4.1 that the decomposition of impacts varies drastically between WTO accession alone and our central scenario of WTO accession combined with cap and trade regulation. WTO
accession alone increases aggregate CO2 emissions by 4.3 percent. The main reason for the increase in
emissions is the increase in output, which increases CO2 emissions of 4.9 percent. The composition effect
also increases emissions in the case of Russia by 1.0 percent. While the composition effect is ambiguous
in general, the reason it has an adverse environmental impact in Russia is that two of the relatively dirty
industries, namely ferrous metals and chemicals and petrochemicals, are (along with nonferrous metals)
the sectors that expand the most.30 Ferrous metals output expands in all nine of the regions in which it
is produced. Output of chemicals and petro-chemicals expands in all ten regions. The reason that these
sectors along with nonferrous metals output expand the most is that trade liberalization, in general, tends
to benefit export-intensive sectors; export-intensive sectors experience the benefit of the removal of the
anti-export bias of trade protection realized through a depreciated real exchange rate. In the case of Russia, it is ferrous metals, nonferrous metals and chemicals that intensively export (two of which intensively
emit CO2 ), while the relatively cleaner sectors, such as food and light industry, which do little exporting,
decline in most regions. While the technique effect works toward reducing CO2 emissions, it is relatively
small at 1.6 percent. The technique effect in this scenario can be traced back to increased productivity with
existing resources due to the Dixit-Stiglitz productivity externality from additional varieties of imperfectly
produced goods and services and a shift toward less fossil fuel intensive forms of production.
29
Although our decomposition is based on a methodology that is a local approximation to a discrete change, we find that the
sum of our components are accurate to within 0.5 percent points of CO2 emissions in all cases except WTO combined with
emissions intensity, where the tolerance is 1.2 percentage points of CO2 emissions.
30
In our dataset, nonferrous metals does not intensively emit CO2 (see Table 1).
12
4.3
Cost of Alternate CO2 Emission Reduction Policies
We take a cleaner environment as an objective desired by the authorities, and assess different environmental
regulations in terms of their economic costs, and by implication, political feasibility. More specifically, we
assess three alternate policies to reduce CO2 emissions by twenty percent from base-year emission levels:
cap and trade; emission intensity standards; and energy efficiency standards. Results of all three policies
are shown for the overall economy average in Table 4.
4.3.1
CO2 Mitigation with “Cap and Trade”
We find that a policy of using a system of tradable emission rights to achieve a twenty percent CO2 emission
reduction would cost 0.5 percent of GDP in aggregate. From our decomposition analysis, we see that the
reduction in CO2 emissions is accomplished primarily through the technique effect. Although there is a
modest 1 percent reduction in emissions through output reduction, and a shift to cleaner industries results
in a 3.1 percent reduction in CO2 emissions, the technique effect accounts for over 16 percent reduction in
CO2 emissions. Since there are virtually no productivity gains in this scenario, unlike our WTO accession
scenario, the technique effect derives from fuel switching toward fuels that emit fewer CO2 emissions and
a switch to a less fuel intensive forms of production.
4.3.2
CO2 Emission Intensity Standards
We find that the impact of requiring a uniform reduction in the intensity of CO2 emissions per unit of
the value of output produced at the sector and regional level (coal, crude oil and gas production excluded)
to achieve a twenty percent reduction in CO2 emissions costs about 0.8 percent of GDP. Again, it is
the technique effect which dominates the adjustment in CO2 emissions, accounting for a 17.1 percent
reduction in CO2 emissions. The switch to clean industries (the composition effect) is much weaker in
this scenario than under emissions trading or energy efficiency standards, since all sectors must reduce
the intensity of their emissions uniformly, even if they start with fewer emissions per ruble of output.
Under this regulation, there is no incentive to switch production to clean industries since it does not help
achieve the regulatory target. Despite this inefficiency, compared to cap and trade, the additional costs of
achieving the twenty percent reduction in CO2 emissions are only 0.3 percent of GDP. The relatively small
additional cost of this “command and control” regulation compared with a market-based system can be
attributed to the fact that “emissions intensity” still targets rather closely the reduction in CO2 emissions.
4.3.3
Energy Efficiency Standards
In this scenario we require that in all regions, all sectors except electricity and fossil fuel production (crude
oil, coal and natural gas) must equi-proportionately reduce their use of gas, refined oil and electricity to
achieve an aggregate twenty percent reduction in CO2 emissions. We find that a uniform requirement to
cut fossil fuel use by 25 percent, results in the exogenously mandated twenty percent reduction in CO2
emissions. The cost in terms of loss in GDP, however, is a very substantial 2.7 percent of GDP, more
than five times the cost of the emissions trading system to achieve the same CO2 emissions reduction. The
higher cost of achieving CO2 emission reduction is partly due to the lack of adjustments of a market-based
system; but our results show that even more important is the fact that, compared with emissions intensity
standards, energy efficiency standards do not closely target the objective of lower CO2 emissions.
On the positive side for energy efficiency standards is that they achieve a much greater reduction in
non-CO2 emissions than CO2 emissions trading or CO2 emissions intensity standards. This is especially
true regarding nitrogen oxide and particulate matter, where energy efficiency standards achieve a double
digit percent reduction. This is in line with the conclusion of Goulder and Parry [2008], who argue that
no single instrument is clearly superior along all dimensions relevant to policy choice.
13
4.4
WTO Accession Joint with CO2 Emissions Reduction Policies
4.4.1
CO2 Emission Markets
When we combine WTO accession with a regulation to cut CO2 emissions by twenty percent through
CO2 emissions trading, we estimate that the overall welfare gains fall to 7.2 percent of consumption compared with 8.6 percent of consumption from WTO accession independently; nonetheless, the overall welfare gains remain high. Interestingly, adding cap and trade regulation on CO2 emissions to WTO accession
results in a change in the sign of the composition effect compared with WTO accession alone. The reason
is that our two dirty industries that were among the strongest expanders under WTO accession alone are
now among the biggest contractors. The chemicals and petro-chemicals sector declines in eight of the ten
regions and the ferrous metals sector declines in all sectors where it is produced. In addition, oil and gas
production as well as pipeline transportation decline in this scenario. The technique effect becomes even
more important in this scenario, as it is responsible for a decline of 19.5 percent in total CO2 emissions.
The Tumen Regional Market is an outlier regarding the strong composition effect contributing to a reduction in its CO2 emissions. This is explained by the fact that Tumen produces 90.8 percent of the gas and
62.8 percent of the crude oil in Russia. In addition to switching away from fossil fuel use, the productivity
gains of WTO accession allow production of output with fewer fossil fuels.
4.4.2
Emissions Intensity Standards
When we combine WTO accession with CO2 emission intensity standards, the estimated welfare gain of
6.4 percent of consumption remains substantial; but this is a further reduction of 0.8 percent of consumption compared with cap and trade combined with WTO accession. As mentioned above, the switch to
clean industries (the composition effect) is relatively weak due to the lack of incentive to switch production to clean industries with this regulation. Consequently, in this policy simulation, the technique effect
is the strongest of all seven of our core policy scenarios.
4.4.3
Energy Efficiency Standards
In this scenario, we combine WTO accession with energy efficiency standards. The welfare gains fall very
substantially to 0.6 percent of consumption. Thus, judged on the basis of CO2 emissions alone, this policy
is by far the most costly. Although emissions intensity standards are more costly than cap and trade for
achieving CO2 emissions reduction, they are much more efficient than energy emission standards. We can
see, however, that energy efficiency standards to achieve CO2 emissions reduction is considerably more
effective at achieving a reduction in emissions other than CO2 . In fact, energy efficiency standards are
more effective at reducing all six non-CO2 emissions.
5
Comparison with CRTS Model Version
In this section, we investigate the impact of our more innovative and empirically accurate IRTS modeling
framework by executing our seven basic scenarios in a model with constant returns to scale and perfect
competition. In Table 5, we show the aggregate results for the overall economy of all of our seven basic
scenarios in a CRTS perfect competition model. Table 5 is directly comparable to Table 4, where the latter
is based on our central model with increasing returns to scale and FDI in services. Crucially, we find there
is a change in the sign of the welfare impact for WTO accession combined with the two least efficient
environmental regulation policies to reduce CO2 emissions.
In either our WTO accession or WTO accession plus environmental regulation combined scenarios,
the estimated welfare gains are substantially larger with the IRTS model. The smaller estimated welfare
gains with the CRTS model are what is expected in models that assess welfare changes through “Harberger
14
triangles.” Crucially, a key result of the analysis with our central IRTS model is reversed with the CRTS
model. We find that with IRTS, WTO accession is expected to expand incomes by more than enough
to pay for the cost of emission reduction, even if the least efficient policy of environmental regulation
is used. With CRTS, simultaneous application of WTO accession and environmental regulation yields
net benefits only if the market-based cap and trade system is employed, and a substantial loss if the least
efficient environmental policy is employed. When simultaneously applied with WTO accession, our least
efficient environmental policy of energy efficiency standards, yields a net welfare loss of 1.6 percent of
GDP. These results show that there are larger net gains available when taking IRTS into account, which
allow for net gains after regulation to produce a net cleaner environment.
On the other hand, assessing WTO accession or the WTO accession plus emission abatement, we see
that the CRTS model shows less of an adverse impact on the environment for all seven pollutants in our
WTO accession scenario. The reason is that the productivity effects of the model with IRTS and FDI leads
to greater output expansion, which results in more emission of pollutants. In the case of CO2 emissions,
our decomposition analysis shows that the scale effect on CO2 emissions, which is the primary culprit in
the increase in CO2 emissions with IRTS, is much smaller in the CRTS model.
6
Piecemeal Sensitivity Analysis
We focus on the scenario in which we combine WTO accession with twenty percent reduction of CO2
emissions through emission trading. For the sake of compactness, Table 6 presents the results for the aggregate economy only. In these scenarios, we retain the central value of all parameters except the parameter
in question. As far as welfare is concerned, the gains to the economy generally increase with an increase
in elasticities, since higher elasticities imply that the economy is able to more easily shift to sectors or
products that are cheaper after trade and FDI liberalization.31 There are two parameters in Table 6 that
show a strong impact on the results: the elasticity of substitution between value-added and business services (ESUBS) and the elasticity of multinational firm supply (ETAF). A liberalization of the barriers to
FDI will result in a reduction in the cost of business services, both from the direct effect of lowering the
costs of doing business for multinational service providers and from the indirect effect that additional varieties of business services allow users to purchase a quality adjusted unit of services at less cost. When the
elasticity of substitution between value-added and business services is high (ESUBS = 2 in Table 6), users
have a greater potential to substitute the cheaper business services and this increases productivity. The
elasticities of multinational and Russian firm supply (ETAF, ETAD) are primarily dependent on the sector
specific factor for each firm type (foreign or domestic). When ETAF is high, a reduction in the barriers
to foreign direct investment results in a larger expansion in the number of multinational firms supplying
the Russian market, and hence more gains from additional varieties of business services. In addition, the
share of the services market captured by multinationals has a strong effect, since a liberalization results in
a larger number of new varieties introduced.
Regarding the sensitivity results for WTO accession alone, in general, both welfare and emissions increase with the elasticities (results not shown). Greater elasticities lead to greater output expansion and
greater emissions. The same pattern tends to hold for our results in Table 6, and our results appear robust. The largest range of results for emissions is with respect to the parameter e u b s. Volatile organic
components, particulate matter, nitrogen oxide and especially hydrocarbons are sensitive to the parameter
ESUBS . For low values of ESUBS , value-added does not substitute well for business services, so when reforms reduce the quality adjusted price of business services, there is less substitution of business services,
then less of a variety externality and less output expansion.
31
An increase in the elasticity of substitution between varieties could reduce the welfare gain. This is because when varieties
are good substitutes, additional varieties are worth less to firms and consumers.
15
7
Conclusions
Using our IRTS model, we find that WTO accession alone will increase environmental pollution in Russia.
We estimate an expansion of dirty industries in Russia and our decomposition shows that the negative effect on the environment of the composition effect (especially the relative expansion of dirty industries) and
the expansion of output (scale effect) dominate the positive impact on the environment of the technique
effect (which includes importing new more efficient technologies, processes and services). We show that
primarily due to a larger output expansion in our IRTS model, there is a larger increase in pollution from
WTO accession alone in our IRTS model compared with a more traditional CRTS model.
Given the evidence of endogenous interaction between trade liberalization and environmental regulation, we assess the costs of three types of environmental regulations to reduce CO2 emissions by twenty
percent, and we execute three simulations in which we combine WTO accession with each of these emission abatement policies. We find that despite the fact that there is a larger increase in pollution with the
IRTS model in first place, CO2 emissions can be reduced by complementary environmental policies while
still retaining substantial welfare gains from trade liberalization. Notably, in a CRTS model, there are no
net welfare gains with the two least efficient environmental regulation policies. This shows that the choice
of the appropriate model is crucial, as even the sign of the impact can be wrong if the wrong model is
chosen. Moreover, while our results are consistent with earlier models that suggest increased pollution
with IRTS models (absent offsetting environmental regulations), our results show that there are greater
gains available when taking IRTS into account, which allow for net gains after regulation to produce a net
cleaner environment.
We find that cap and trade is the most efficient policy for CO2 emissions, as expected. Emissions
intensity standards are less efficient than cap and trade in part since there is no incentive to switch sectors.
But we find that the costs of emissions intensity standards are drastically lower than the costs of energy
efficiency standards, as the former operate more closely to the relevant margin.
While many studies have compared the cost-effectiveness of different emission abatement policies using
CGE models, our analysis – to our best knowledge – is the first general equilibrium assessment to compare
the economic efficiency of different environmental regulations using an IRTS model. This paper highlights
that when we estimate the combined impact of trade liberalization and environmental policies, even the
sign of the result depends on the choice of the market structure.
16
Table 1: Sectoral Emission Intensities
CO2 emission intensity: kilograms per ruble; non-CO2 emissions intensity: grams per ruble.
Total
CO2 Intensity
Direct
Indirect
(from
Electricity)
Emission Intensity of non-CO2 Pollutants
Sulphur
Dioxide
Hydrocarbons
Carbon
Monoxide
Nitrogen
Oxide
Particulate
Matter
Volatile
Organic
Components
Imperfectly Competitive Goods
Nonferrous Metals (NFM)
Ferrous Metals (FME)
Chemicals (CHM)
Food Industry (FOO)
Metal Working (MWO)
Timber, Wood and Paper (TPP)
Other Industries (OTI)
Construction Materials (CNM)
0.6
3.5
2.7
0.5
1.0
1.3
1.0
3.1
0.3
3.1
2.0
0.4
0.8
0.9
0.7
2.5
0.3
0.4
0.7
0.1
0.3
0.3
0.2
0.5
4.3
1.9
0.2
0.0
0.0
0.0
0.0
0.1
0.0
0.0
0.1
0.0
0.0
0.0
0.0
0.0
1.4
1.1
0.5
0.1
0.2
0.1
0.0
0.6
0.2
0.1
0.2
0.0
0.1
0.0
0.0
0.3
0.5
0.3
0.3
0.1
0.2
0.1
0.0
1.0
0.0
0.0
0.2
0.0
0.0
0.0
0.0
0.0
Perfectly Competitive Goods and Services
Electricity (ELE)
Oil Refining (OIL)
Crude Oil (CRU)
Textiles and Apparel (CLI)
Housing (HOU)
Coal (COL)
Gas (GAS)
Agriculture and Forestry (AGR)
Other Goods Sectors (OTH)
Public Services, Culture (HEA)
Construction (CON)
Post (PST)
Trade, Wholesale and Retail (TRD)
7.9
0.5
0.5
0.6
3.7
5.7
5.7
0.5
0.2
0.9
0.5
0.2
0.2
7.2
0.3
0.3
0.3
2.9
5.4
5.6
0.4
0.1
0.5
0.5
0.1
0.2
0.7
0.2
0.2
0.3
0.8
0.2
0.2
0.1
0.0
0.4
0.1
0.1
0.0
2.8
0.3
0.2
0.1
0.1
0.1
0.0
0.0
0.0
x*
x*
x*
x*
0.0
0.1
1.4
0.0
0.0
3.7
0.2
0.0
0.0
x*
x*
x*
x*
0.4
0.2
3.8
0.2
0.1
0.1
0.3
0.1
0.0
x*
x*
x*
x*
1.8
0.1
0.2
0.1
0.0
0.1
0.0
0.0
0.0
x*
x*
x*
x*
2.3
0.1
0.7
0.2
0.0
0.3
0.0
0.1
0.0
x*
x*
x*
x*
0.0
0.3
1.2
0.0
0.0
0.0
0.1
0.0
0.0
x*
x*
x*
x*
Imperfectly Competitive Business Services with FDI
Maritime Transportation (MAR)
0.6
Railway Transportation (RLW)
0.9
Truck Transportation (TRK)
1.3
Air Transportation (AIR)
1.6
Other Transportation (TRO)
0.9
Pipleline Transportation (PIP)
7.0
Telecommunications (TMS)
0.2
Science and Engineering (SCI)
0.7
Financial Services (FIN)
0.9
0.6
0.4
1.2
1.5
0.5
6.3
0.1
0.5
0.7
0.1
0.4
0.1
0.1
0.3
0.7
0.1
0.1
0.2
0.1
0.1
0.1
0.1
0.1
0.1
x*
x*
x*
1.5
1.3
1.2
1.1
1.2
1.5
x*
x*
x*
0.3
0.3
0.3
0.2
0.3
0.3
x*
x*
x*
0.2
0.1
0.1
0.1
0.1
0.2
x*
x*
x*
0.2
0.2
0.2
0.2
0.2
0.2
x*
x*
x*
0.1
0.1
0.1
0.1
0.1
0.1
x*
x*
x*
Emission Averages
Final Consumption
Sector Average
Average Including Final Consumption
0.1
1.0
0.8
0.1
0.2
0.2
0.3
0.2
0.3
0.1
0.2
0.1
0.2
1.3
1.0
* Non-CO2 pollution data are unavailable for these sectors.
Sources: SUST-RUS database, described in SUST-RUS (2012c 2012b, 2012a) and Böhringer, Thomas F. Rutherford, and Turdyeva (2014, Appendix
A); Rogidromet (2013, Table A3.3); of Natural Resources and of the Russian Federation [2007]; and Rosstat online database at: http://www.gks.ru.
17
Table 2: Pre- and Post-WTO Accession Trade Distortions - (ad valorem %)
Tariff rates
2011-Pre-WTO accession 2020-final commitments
Electric Industry
Oil Extraction
Oil Processing
Gas
Coal Mining
Ferrous Metallurgy
nonferrous Metallurgy
Chemical & Oil-chemical Industry
Mechanical Engineering & Metal-working
Timber & Woodworking & Pulp & Paper Industry
Construction Materials Industry
Textiles and Apparel
Food Industry
Other Industries
Agriculture & Forestry
Other Goods-producing Sectors
Telecommunications
Science & Science Servicing (Market)
Financial Services
Railway Transportation
Truck Transportation
Pipelines Transportation
Maritime Transportation
Air Transportation
Other Transportation
0.0
1.7
5.1
4.7
4.4
8.6
10
7.4
8.9
14.3
12.7
12.3
18.2
10.4
7.7
14.2
0.0
1
4.9
5
4.4
5.9
7.4
5.2
5.7
8.2
9.9
8.2
13.6
7.4
5.7
10.7
Export
tax rates
Change in
world market price
0.0
7.9
4.6
18.8
0.0
0.4
5.3
1.6
0.0
6.9
1.6
4.1
3.1
0.0
0.6
0.0
0.0
0.0
0.0
0.0
0.0
1.5
1.5
1.5
0.0
0.0
0.0
0.5
0.5
0.5
0.0
0.5
Effective barriers to FDI (%)
Pre-WTO Accession
Post-WTO Accession
33.0
33.0
36.0
33.0
33.0
33.0
95.0
90.0
33.0
0.0
0.0
0.0
0.0
0.0
0.0
80.0
75.0
0.0
Source: Shepotylo and Tarr [2013] for tariff rates; Jensen, Rutherford, and Tarr [2004] for barriers to FDI; Roskomstat for export tax rates; authors’ estimates for change in world market prices.
18
Table 3: WTO Accession with CO2 Cap and Trade – IRTS Model
Overall
average
Moscow
St.Peters.
Tumen
Northwest
North
Central
South
Urals
Siberia
Far East
7.2
3.3
5.5
3.0
9.7
4.4
13.7
2.9
12.5
5.8
9.2
3.8
8.4
3.9
7.4
3.5
6.4
2.9
7.5
3.5
8.5
3.8
0.0
-20.0
3.6
-3.6
-19.5
112.4
-19.0
3.5
-1.0
-20.8
112.4
-17.7
5.9
-0.7
-21.6
112.4
-22.0
2.3
-11.4
-13.1
112.4
-18.8
6.0
-2.7
-20.9
112.4
-18.1
5.2
-3.4
-19.1
112.4
-19.1
4.0
-1.3
-21.1
112.4
-17.8
4.0
-1.5
-19.7
112.4
-21.3
2.5
-3.0
-20.7
112.4
-20.8
3.8
-5.2
-19.1
112.4
-18.3
5.3
-2.9
-19.8
Non-CO2 Emissions (% Change)
Sulphur dioxide
Nitrogen oxide
Hydrocarbons
Particulate matter
Volatile organic components
Carbon monoxide
5.9
-1.3
-3.6
-0.6
0.8
2.2
1.6
-2.9
-4.7
-3.2
-1.6
9.5
11.7
0.3
0.6
-0.4
2.2
6.0
-2.6
-1.4
-0.5
-0.3
0.9
0.7
-8.6
1.3
1.2
0.1
17.6
-9.2
7.4
-0.1
-3.4
0.7
1.4
4.1
-2.9
-1.9
-2.0
-2.1
9.2
-3.4
2.7
-1.6
-1.7
-2.1
1.9
2.3
5.3
-1.7
-2.8
-1.2
-0.1
1.8
7.3
0.0
-10.7
1.0
-1.5
5.2
5.2
-1.0
-16.7
-0.4
3.3
3.1
Aggregate Trade (% Change)
Regional terms of trade
Regional exports
Real exchange rate
International exports
2.8
1.4
2.4
8.2
14.0
1.5
2.5
16.0
15.5
2.3
3.3
13.6
12.6
0.9
1.9
3.9
14.6
2.4
3.5
12.7
14.5
1.9
2.6
9.0
13.7
2.5
3.2
10.0
13.4
1.6
2.6
9.6
12.4
1.4
1.9
8.9
13.0
0.4
1.7
7.2
14.6
2.1
2.6
13.1
1.6
0.3
1.6
3.6
3.8
-33.4
-9.2
0.0
0.0
2.2
0.7
1.7
3.4
0.0
0.0
0.0
-29.9
59.3
4.4
3.7
2.5
6.3
0.0
0.0
0.0
-21.5
48.4
1.0
-1.2
1.1
4.2
3.6
-34.1
0.0
-48.5
217.4
4.8
3.2
2.7
5.8
5.8
-28.4
0.0
-21.9
77.4
2.7
1.3
1.8
5.2
5.3
-30.6
-6.5
-25.5
139.6
2.2
1.6
2.4
3.4
0.0
0.0
-4.7
-17.4
113.4
2.3
1.5
1.8
3.7
5.5
-23.9
-6.2
-25.0
125.9
0.0
-0.4
1.1
2.4
3.4
-26.3
-9.9
-18.2
139.0
1.0
-1.8
0.9
3.7
2.9
-34.1
-10.0
-18.8
159.2
3.2
1.7
1.8
5.7
5.3
-33.6
-7.0
-27.1
113.6
2.1
2.9
1.8
2.4
2.1
2.6
2.5
3.1
2.8
3.3
2.3
3.4
1.8
2.3
1.4
1.9
2.4
2.7
2.6
4.4
3.2
4.4
Aggregate Welfare
Welfare (EV as % of consumption)
Welfare (EV as % of GDP)
CO2 Emissions and Decomposition
CO2 price (ruble per ton of CO2 )
CO2 emissions (% change), decomposed into:
- Output effect (% of CO2 )
- Composition effect (% of CO2 )
- Technique effect (% of CO2 )
Return to Primary Factors (% Change)
Unskilled labor
Skilled labor
National capital
Regional mobile capital
Crude oil resources
Natural gas resources
Coal resources
Specific capital in domestic firms
Specific capital in multinational firms
Factor Adjustments
Unskilled labor (% changing sectors)
Skilled labor (% changing sector)
Source: Authors’ estimates
19
Table 4: Nation-Wide Impacts – IRTS Model
WTO
Accession
Only
WTO Accession plus:
CO2
Emissions
Energy
Emissions
Intensity Intensity
Cap and Trade Standards Standards
CO2 Reduction Policies Alone:
CO2
Emissions
Energy
Emissions
Intensity Intensity
Cap and Trade Standards Standards
Aggregate Welfare
Welfare (EV as % of consumption)
Welfare (EV as % of GDP)
8.6
4.0
7.2
3.3
6.4
3.0
0.6
0.3
-1.1
-0.5
-1.7
-0.8
-5.9
-2.7
CO2 Emissions and Decomposition
CO2 price (ruble per ton of CO2 )
CO2 emissions, decomposed into:
- Output effect (% of CO2 )
- Composition effect (% of CO2 )
- Technique effect (% of CO2 )
4.3
4.9
1.0
-1.6
112.4
-20.0
3.6
-3.6
-19.5
-19.2
2.7
-0.4
-20.3
-19.7
-3.0
-6.5
-10.6
96.0
-20.0
-1.0
-3.1
-16.3
-20.0
-1.8
-1.0
-17.1
-19.8
-6.1
-6.0
-8.2
Non-CO2 Emissions (% Change)
Sulphur dioxide
Nitrogen oxide
Hydrocarbons
Particulate matter
Volatile organic components
Carbon monoxide
6.2
2.8
1.7
3.0
2.7
4.5
5.9
-1.3
-3.6
-0.6
0.8
2.2
7.1
5.1
-2.6
4.3
1.1
3.0
-1.3
-16.4
-6.4
-12.0
-5.8
-1.2
-0.1
-3.3
-4.2
-2.9
-1.5
-1.7
1.0
2.0
-3.6
1.1
-1.4
-1.2
-6.9
-16.1
-6.5
-12.5
-6.8
-4.7
Aggregate Trade (% Change)
Regional terms of trade
Regional exports
Real exchange rate
International exports
2.9
3.0
1.8
8.1
2.8
1.4
2.4
8.2
2.2
1.4
1.5
7.5
0.9
-6.2
1.6
6.3
-0.1
-1.2
0.4
0.4
-0.6
-1.3
-0.3
-0.4
-1.5
-7.5
-0.2
-1.2
Return to Primary Factors (% Change)
Unskilled labor
Skilled labor
National capital
Regional mobile capital
Crude oil resources
Natural gas resources
Coal resources
3.7
3.7
3.8
5.8
4.4
7.0
9.7
1.6
0.3
1.6
3.6
3.8
-33.4
-9.2
3.6
3.9
2.4
4.9
1.6
-44.2
-0.7
3.1
2.5
-0.8
1.5
-3.3
-33.1
5.0
-1.7
-2.6
-1.7
-1.8
-0.6
-32.9
-13.6
0.0
0.3
-1.1
-0.7
-2.4
-42.7
-7.9
0.0
-0.4
-3.4
-3.1
-6.4
-32.0
-3.2
Factor Adjustments
Unskilled labor (% changing sectors)
Skilled labor (% changing sector)
2.0
2.6
2.1
2.9
2.1
2.8
2.9
3.5
0.9
1.5
0.7
1.1
2.2
2.7
Source: Authors’ estimates
20
Table 5: Nation-Wide Impacts – CRTS Model
WTO
Accession
Only
WTO Accession plus:
CO2
Emissions
Energy
Emissions
Intensity Intensity
Cap and Trade Standards Standards
CO2 Reduction Policies Alone:
CO2
Emissions
Energy
Emissions
Intensity Intensity
Cap and Trade Standards Standards
Aggregate Welfare
Welfare (EV as % of consumption)
Welfare (EV as % of GDP)
1.1
0.5
0.7
0.3
0.0
0.0
-3.5
-1.6
-0.4
-0.2
-1.0
-0.4
-4.1
-1.9
CO2 Emissions and Decomposition
CO2 price (ruble per ton of CO2 )
CO2 emissions, decomposed into:
- Output effect (% of CO2 )
- Composition effect (% of CO2 )
- Technique effect (% of CO2 )
1.0
1.1
0.4
-0.4
107.8
-20.0
0.4
-2.4
-18.0
-20.4
-0.4
-0.9
-18.6
-20.2
-4.7
-6.2
-9.7
103.4
-20.0
-0.7
-2.5
-17.2
-19.9
-1.4
-1.2
-17.3
-19.9
-5.3
-6.1
-8.8
Non-CO2 Emissions (% Change)
Sulphur dioxide
Nitrogen oxide
Hydrocarbons
Particulate matter
Volatile organic components
Carbon monoxide
4.6
0.9
0.1
1.1
0.1
1.9
4.5
-2.1
-4.5
-1.4
-1.1
0.5
5.2
3.1
-3.8
2.4
-1.0
0.6
-4.7
-16.5
-7.1
-12.5
-6.7
-3.6
-0.4
-2.9
-4.3
-2.5
-1.1
-1.4
0.6
2.1
-3.6
1.2
-1.1
-1.2
-8.9
-16.4
-6.7
-12.9
-6.4
-5.2
Aggregate Trade (% Change)
Regional terms of trade
Regional exports
Real exchange rate
International exports
1.0
0.2
0.2
2.1
1.1
-0.9
0.7
2.0
0.7
-1.0
0.0
1.7
0.1
-7.7
0.4
-0.1
0.1
-1.1
0.4
-0.1
-0.3
-1.1
-0.2
-0.4
-0.8
-7.4
0.2
-2.0
Return to Primary Factors (% Change)
Unskilled labor
Skilled labor
National capital
Regional mobile capital
Crude oil resources
Natural gas resources
Coal resources
1.2
1.6
1.4
1.9
0.2
0.4
0.6
-0.3
-0.9
-0.1
0.2
-0.1
-35.4
-12.2
1.7
2.4
0.7
1.6
-1.6
-45.9
-7.6
2.7
2.5
-1.2
-0.3
-5.8
-35.2
-0.5
-1.5
-2.4
-1.5
-1.6
-0.3
-33.8
-12.2
0.5
0.8
-0.7
-0.2
-1.7
-43.0
-7.3
1.5
0.9
-2.4
-1.9
-5.7
-32.8
-0.9
Factor Adjustments
Unskilled labor (% changing sectors)
Skilled labor (% changing sector)
0.8
0.9
0.9
1.4
1.2
1.5
2.5
3.0
0.7
1.3
0.7
1.0
2.3
2.7
Source: Authors’ estimates
21
Table 6: Sensitivity Analysis – IRTS Model
Parameter being changed
Welfare Effects
EV % of:
consumption GDP
Total
CO2
CO2 Emissions (% Change)
Output Composition Technique
Effect
Effect
Effect
Sulphur
Dioxide
Non-CO2 Emissions (% Change)
Nitrogen Hydro- Particulate Volatile
Oxide
carbons
Matter
Organics
Carbon
Monoxide
Central Results for reference
7.2
3.3
-20.0
3.6
-3.6
-19.5
5.9
-1.3
-3.6
-0.6
0.8
2.2
esubconsumer = 1.5
esubconsumer = 0.5
7.4
6.5
3.5
3.0
-20.0
-20.0
3.7
3.2
-3.7
-3.2
-19.6
-19.5
5.8
6.0
-1.4
-1.0
-3.4
-4.1
-0.6
-0.4
0.8
0.7
2.2
2.2
esubs = 2.0
esubs = 0.5
9.5
5.7
4.4
2.6
-20.0
-20.0
5.6
2.2
-3.8
-3.4
-21.0
-18.6
6.3
5.6
0.0
-2.1
-1.1
-5.2
0.4
-1.2
1.6
0.2
2.8
1.8
sigmadm = 4
sigmadm = 2
7.3
6.3
3.4
2.9
-20.0
-20.0
3.5
3.3
-3.7
-3.3
-19.4
-19.6
6.1
5.9
-1.2
-1.6
-3.8
-3.3
-0.4
-1.0
0.9
0.2
2.4
1.8
etaf = 17.5
etaf = 12.5
8.1
6.1
3.8
2.8
-20.0
-20.0
4.1
2.9
-3.6
-3.5
-19.9
-19.1
5.7
6.0
-1.1
-1.6
-3.4
-3.8
-0.5
-0.8
1.2
0.3
2.4
2.0
etad = 10
etad = 5
7.5
6.8
3.5
3.1
-20.0
-20.0
3.4
3.8
-3.8
-3.3
-19.2
-20.0
7.1
4.5
-1.3
-1.2
-4.1
-2.9
-0.5
-0.6
0.5
1.1
2.6
1.8
esub = 4
esub = 2
7.2
6.2
3.4
2.9
-20.0
-20.0
3.5
3.4
-3.6
-3.3
-19.4
-19.6
6.0
5.7
-1.3
-1.6
-3.8
-3.4
-0.5
-0.9
1.0
0.1
2.5
1.6
esubprimary = 1.5
esubprimary = 0.5
7.2
7.2
3.3
3.3
-20.0
-20.0
3.6
3.6
-3.6
-3.5
-19.6
-19.5
6.0
5.5
-1.3
-1.2
-3.8
-3.2
-0.6
-0.5
0.6
1.1
2.2
2.3
etadx = 7
etadx = 3
7.2
7.0
3.4
3.3
-20.0
-20.0
3.6
3.5
-3.8
-3.3
-19.4
-19.8
5.9
5.7
-1.4
-1.2
-3.7
-3.5
-0.7
-0.4
0.7
0.9
2.2
2.2
Source: Authors’ estimates
Key:
esubconsumer
esubs
sigmadm
Etaf
Etad
esub
esubprimary
Etadx
Central value
1
1.25
3
15
7.5
3
1
5
Definition
Elasticity of substitution in consumer demand
Elasticity of substitution between value-added and business services
"Armington" elasticity of substitution between imports and domestic goods in CRTS sectors
Elasticity of multinational service firm supply with respect to price of output
Elasticity of Russian service firm supply with respect to price of output
Elasticity of substitution between firm varieties in imperfectly competitive sectors
Elasticity of substitution between primary factors of production in value added
Elasticity of transformation (domestic output versus exports)
22
23
Business
Service 1
Business
Service S
GAS
Russian Service
Providers of s in RM r
CES
σ =3
Russian firms
Multinational firms
Rest of World
Cross-Border Services of s
CES
σ =3
CES
σ =3
Regional Market r
Services of s
CES
σ = 1.5
Business
Service s
Multinational Service
Providers of s in RM r
Unskilled
Labor
OIL
(refined)
CES
σ =3
Cobb-Douglas
σ =1
Capital
Gas-Oil Composite
Business Services
Value-added
Rest of World firms
selling g im RM r
CES
σ =3
Imports of g from
Rest of the World
Electricity
Cobb-Douglas
σ =1
Energy Composite
Cobb-Douglas
σ =1
Skilled
Labor
Coal
IRTS
Good 1
CES
σ =3
Goods g from
RM m
CES
σ =3
Goods g from
RM M
b. We take σ = esub = 3, except based on Ivanova (2005), we take σ = 3.1 in MWO; σ = 2.6 in TPP; σ = 2.5 in CNM; and σ = 1.8 in OTI.
CES
σ =3
CRTS
Good h
CES
σ = 4.5
Imports of h from RM m
CES
σ =6
CRTS
Good H
Regional
Market r good h
Russian goods h
Other Russian
Imports of h
Imports of h
from
Rest of World
CRTS
Good 1
Firms in RM 1
Firms in RM m Firms in RM M
selling g im RM r selling g im RM r selling g im RM r
CES
σ =3
Goods g from
RM 1
CES
σ = 1.5
IRTS
Good G
Leontief
σ =0
Leontief
σ =0
IRTS
Good g
Other CRTS
Goods and Services
Other IRTS
Goods
Leontief
σ =0
a. For business services, there is only supply to the home Regional market and to the Rest of the World.
Note:
Sector
Specific
Resources
CES
σ = 1.25
Value-added and Business Services
Composite of Other
Goods and Services
Supply to
Regional Market r
CES
σ = 0.5
Leontief
σ =0
Output
σ =5
CET
Supply to
Other Regional Markets
Composite of Value-added,
Business Services, and Energy
Exports to
Rest of World
Figure 1: Production Structure for non-Fossil Fuel Sectors
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